Irrigation Planning for Development of an Effective Cropping Pattern Using Genetic Algorithm

Sumant A. Choudhari; Manoj A. Kumbhalkar; Mhalsakant M. Sardeshmukh; Dattatraya V. Bhise

doi:10.31893/multiscience.2024083

Keywords:

Linear Programming Method

Abstract

The majority of surface irrigation schemes are diverse in character, consisting of a diversity of crops and soils as well as a huge network of canals with varying qualities (design capacities, efficiencies, command area, length, duration of operation, etc.). The programs in semiarid and arid locations are similarly related to limited water supplies and operate on a rotating water distribution system. As a result, managing irrigation in such settings is tough. It demands decisions on how much water and space should be allotted to different crops grown on different soils and in different areas or regions of the scheme (the allocation plan), based on water availability, benefit maximization, varied needs, and the physical boundaries of the scheme. The current study focuses on the use of Genetic Algorithms (GA) to irrigation planning. In India, the GA technique is being used to create an effective farming plan for an irrigation project. Constraints include land and water limitations, as well as crop and storage limits. The model is run for various choices of population, generations, cross over, and mutation probabilities to determine GA parameters. The results of GA are compared to those of Linear Programming. This case study is about a problem with linear constraints that was addressed using a genetic algorithm. The model's future use will be to address issues with non-linear constraints. Traditional nonlinear programming approaches become difficult and time intensive in such instances. Future research is being conducted to improve the efficiency and usability of these artificial intelligence systems.
References
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18. Wu, Z. Y. and Simpson, A. R., ‘Competent genetic-evolutionary optimization of water distribution systems’, J. Comp. Civil Engin. ASCE, 2001, Volume15, pp 89–101. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:2(89)
19. D. Nagesh Kumar, K. Srinivasa Raju and B. Ashok, ‘Optimal Reservoir Operation for Irrigation of Multiple Crops Using Genetic Algorithms’, Journal of Irrigation and Drainage Engineering, Vol. 132, No. 2, April 1, 2006. https://doi.org/10.1061/(ASCE)0733-9437(2006)132:2(123)
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22. S. A. Choudhari, M. A. Kumbhalkar, D. V. Bhise, M. M. Sardeshmukh, “Optimal Reservoir Operation Policy Determination for Uncertainty Conditions”, 3C Empresa, Ed. 50 Vol. 11 No. 2, August - December 2022, pp 277-295. https://doi.org/10.17993/3cemp.2022.110250.277-295

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Choudhari, S. A., Kumbhalkar, M. A., Sardeshmukh, M. M., & Bhise, D. V. (2023). Irrigation Planning for Development of an Effective Cropping Pattern Using Genetic Algorithm. Multidisciplinary Science Journal, (| Accepted Articles), 2024083. https://doi.org/10.31893/multiscience.2024083

[1] Sonal Jain, Dharavath Ramesh, Diptendu Bhattacharya, ‘A multi-objective algorithm for crop pattern optimization in agriculture’, Applied Soft Computing, Volume 112, November 2021, 107772. https://doi.org/10.1016/j.asoc.2021.107772

[2] Katoch, S., Chauhan, S.S. & Kumar, V., ‘A review on genetic algorithm: past, present, and future’, Multimed Tools Appl, 2021, 80, 8091–8126. https://doi.org/10.1007/s11042-020-10139-6

[3] Evangelia V. Avramidou, Georgios C. Koubouris, Panos V. Petrakis, Katerina K. Lambrou, Ioannis T. Metzidakis and Andreas G. Doulis, ‘Classification Binary Trees with SSR Allelic Sizes: Combining Regression Trees with Genetic Molecular Data in Order to Characterize Genetic Diversity between Cultivars of Olea europaea L.’, Agronomy 2020, 10, 1662. https://doi.org/10.3390/agronomy10111662

[4] Enes Ayan, Hasan Erbay, Fatih Varçın, ‘Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks’, Computers and Electronics in Agriculture, Volume 179, December 2020, 105809. https://doi.org/10.1016/j.compag.2020.105809

[5] Clementine Namazzi, Julius Pyton Sserumaga, Swidiq Mugerwa, Martina Kyalo, Collins Mutai, Robert Mwesigwa, Appolinaire Djikeng, and Sita Ghimire, ‘Genetic Diversity and Population Structure of Brachiaria (syn. Urochloa) Ecotypes from Uganda’, Agronomy, 2020, 10(8), 1193. https://doi.org/10.3390/agronomy10081193

[6] Ahmed Mellouli, Racem Mellouli & Faouzi Masmoudi, ‘An Innovative Genetic Algorithm for a Multi-Objective Optimization of Two-Dimensional Cutting-Stock Problem’, Applied Artificial Intelligence, Volume 33, 2019 - Issue 6. https://doi.org/10.1080/08839514.2019.1583857

[7] Nabin Narayan Munankarmi, Neesha Rana, Tribikram Bhattarai, Ram Lal Shrestha, Bal Krishna Joshi, Bikash Baral and Sangita Shrestha, ‘Characterization of the Genetic Diversity of Acid Lime (Citrus aurantifolia (Christm.) Swingle) Cultivars of Eastern Nepal Using Inter-Simple Sequence Repeat Markers’, Plants 2018, 7(2), 46; https://doi.org/10.3390/plants7020046

[8] Xingtan Zhang, Xuequn Chen, Pingping Liang and Haibao Tang, ‘Cataloguing Plant Genome Structural Variations’, Curr. Issues Mol. Biol., 2018, 27(1), 181-194; https://doi.org/10.21775/cimb.027.181

[9] Reem F. Digna, Mario E. Castro-Gama, Pieter Van der Zaag, Yasir A. Mohamed, Gerald Corzo and Stefan Uhlenbrook, ‘Optimal Operation of the Eastern Nile System Using Genetic Algorithm, and Benefits Distribution of Water Resources Development’, Water, 2018, 10(7), 921; https://doi.org/10.3390/w10070921

[10] Iosvany Recio Villa, José Bienvenido Martínez Rodríguez, José-Luis Molina and Julio César Pino Tarragó, ‘Multiobjective Optimization Modeling Approach for Multipurpose Single Reservoir Operation’, Water, 2018, 10(4), 427; https://doi.org/10.3390/w10040427

[11] Ghasemi, M.M., Karamouz, M. & Shui, L.T. ‘Farm-based cropping pattern optimization and conjunctive use planning using piece-wise genetic algorithm (PWGA): a case study’ Model. Earth Syst. Environ., 2016, 2, 25. https://doi.org/10.1007/s40808-016-0076-z

[12] González Perea, R., Camacho Poyato, E., Montesinos, P. et al., ‘Optimization of Irrigation Scheduling Using Soil Water Balance and Genetic Algorithms’, Water Resour Manage, 2016, 30, 2815–2830. https://doi.org/10.1007/s11269-016-1325-7

[13] Somayeh Khanjari Sadati, Stijn Speelman, Mahmood Sabouhi, Mohsen Gitizadeh and Bijan Ghahraman, ‘Optimal Irrigation Water Allocation Using a Genetic Algorithm under Various Weather Conditions’, Water 2014, 6(10), 3068-3084; https://doi.org/10.3390/w6103068

[14] H.Md. Azamathulla, Fu-ChunWu, Aminuddin Ab Ghani, Sandeep M. Narulkar, Nor Azazi Zakaria, Chun Kiat Chang, ‘Comparison between genetic algorithm and linear programming approach for real time operation’, Journal of Hydro-environment Research, Volume 2, Issue 3, 12 December 2008, Pages 172-181. https://doi.org/10.1016/j.jher.2008.10.001

[15] Sheng-FengKuo, Gary PMerkley, Chen-WuingLiu, ‘Decision support for irrigation project planning using a genetic algorithm’, Agricultural Water Management, Volume 45, Issue 3, August 2000, Pages 243-266. https://doi.org/10.1016/S0378-3774(00)00081-0

[16] Benyamin Khoshnevisan, Elham Bolandnazar, Shahaboddin Shamshirband, Hanifreza Motamed Shariati, Nor Badrul Anuar, Miss Laiha Mat Kiah, ‘Decreasing environmental impacts of cropping systems using life cycle assessment (LCA) and multi-objective genetic algorithm’, Journal of Cleaner Production, Volume 86, 1 January 2015, Pages 67-77. https://doi.org/10.1016/j.jclepro.2014.08.062

[17] K. Srinivasa Raju and D. Nagesh Kumar, ‘Irrigation Planning using Genetic Algorithms’, Water Resources Management,2004, Vol.18, No. 2, pp.163–176. https://doi.org/10.1023/B:WARM.0000024738.72486.b2

[18] Wu, Z. Y. and Simpson, A. R., ‘Competent genetic-evolutionary optimization of water distribution systems’, J. Comp. Civil Engin. ASCE, 2001, Volume15, pp 89–101. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:2(89)

[19] D. Nagesh Kumar, K. Srinivasa Raju and B. Ashok, ‘Optimal Reservoir Operation for Irrigation of Multiple Crops Using Genetic Algorithms’, Journal of Irrigation and Drainage Engineering, Vol. 132, No. 2, April 1, 2006. https://doi.org/10.1061/(ASCE)0733-9437(2006)132:2(123)

[20] Ujwal G. Patil, S. N. Patil., ‘Need of Evaluation of the Present Status of Environmental Conditions of Completed River Valley Projects – Jayakwadi Project a Case Study’, International Journal of Recent Trends in Science and Technology, 2013, Volume 6, pp 23-31.

[21] Nicklow, J.W., Ozkurt, O. & Bringer, J.A., ‘Control of Channel Bed Morphology in Large-Scale River Networks using a Genetic Algorithm’, Water Resources Management, 17, 113–132, 2003. https://doi.org/10.1023/A:1023609806431

[22] S. A. Choudhari, M. A. Kumbhalkar, D. V. Bhise, M. M. Sardeshmukh, “Optimal Reservoir Operation Policy Determination for Uncertainty Conditions”, 3C Empresa, Ed. 50 Vol. 11 No. 2, August - December 2022, pp 277-295. https://doi.org/10.17993/3cemp.2022.110250.277-295

Abstract

References

How to cite